app.py

import gradio as gr
import operator
import torch
from transformers import BertTokenizer, BertForMaskedLM

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = BertTokenizer.from_pretrained("shibing624/macbert4csc-base-chinese")
model = BertForMaskedLM.from_pretrained("shibing624/macbert4csc-base-chinese")
model.to(device)


def ai_text(texts):
    with torch.no_grad():
        outputs = model(**tokenizer(texts, padding=True, return_tensors='pt').to(device))

    def get_errors(corrected_text, origin_text):
        sub_details = []
        for i, ori_char in enumerate(origin_text):
            if ori_char in [' ', '“', '”', '‘', '’', '琊', '\n', '…', '—', '擤']:
                # add unk word
                corrected_text = corrected_text[:i] + ori_char + corrected_text[i:]
                continue
            if i >= len(corrected_text):
                continue
            if ori_char != corrected_text[i]:
                if ori_char.lower() == corrected_text[i]:
                    # pass english upper char
                    corrected_text = corrected_text[:i] + ori_char + corrected_text[i + 1:]
                    continue
                sub_details.append((ori_char, corrected_text[i], i, i + 1))
        sub_details = sorted(sub_details, key=operator.itemgetter(2))
        return corrected_text, sub_details

    result = []
    for ids, text in zip(outputs.logits, texts):
        _text = tokenizer.decode(torch.argmax(ids, dim=-1), skip_special_tokens=True).replace(' ', '')
        corrected_text = _text[:len(text)]
        corrected_text, details = get_errors(corrected_text, text)
        print(text, ' => ', corrected_text, details)
        result.append((corrected_text, details))
    print(result)
    return result


examples = [
    ['真麻烦你了。希望你们好好的跳无'],
    ['少先队员因该为老人让坐'],
    ['机七学习是人工智能领遇最能体现智能的一个分知'],
    ['今天心情很好',
     '老是较书。'],
    ['遇到一位很棒的奴生跟我聊天。'],
    ['他的语说的很好，法语也不错'],
    ['他法语说的很好，的语也不错'],
    ['他们的吵翻很不错，再说他们做的咖喱鸡也好吃'],
    ['不过在许多传统国家，女人向未得到平等'],
]

output_text = gr.outputs.Textbox()
gr.Interface(ai_text, "textbox", output_text, title="Chinese Text Correction shibing624/macbert4csc-base-chinese",
             description="Copy or input error Chinese text. Submit and the machine will correct text.",
             examples=examples).launch()